AI-Trained Vehicles Can Adjust to Extreme Turbulence on the Fly

Researchers at Caltech took an important step toward using reinforcement learning to adaptively learn how turbulent wind can change over time, and then uses that knowledge to control a UAV based on what it is experiencing in real time.

California Institute of Technology, Pasadena, CA

In nature, flying animals sense coming changes in their surroundings, including the onset of sudden turbulence, and quickly adjust to stay safe. Engineers who design aircraft would like to give their vehicles the same ability to predict incoming disturbances and respond appropriately. Indeed, disasters such as the fatal Singapore Airlines flight this past May in which more than 100 passengers were injured after the plane encountered severe turbulence, could be avoided if aircraft had such automatic sensing and prediction capabilities combined with mechanisms to stabilize the vehicle.

Now a team of researchers from Caltech's Center for Autonomous Systems and Technologies (CAST) and NVIDIA has taken an important step toward such capabilities. In a new paper published in the journal NPJ Robotics, the team describes a control strategy they have developed for unmanned aerial vehicles, or UAVs, called FALCON (Fourier Adaptive Learning and CONtrol). The strategy uses reinforcement learning, a form of artificial intelligence, to adaptively learn how turbulent wind can change over time and then uses that knowledge to control a UAV based on what it is experiencing in real time.